REINFORCEMENT LEARNING: METHODS AND RECENT APPLICATIONS

Abstract

This comprehensive analysis highlights the potential of Reinforcement Learning (RL) to transform intelligent decision-making systems by examining its techniques and applications in a variety of disciplines. The study offers a thorough examination of the advantages and disadvantages of several reinforcement learning (RL) approaches, such as Q-Learning, Deep Q-Networks (DQN), Policy Gradient Methods, and Model-Based RL. The paper explores RL applications in several domains, including robotics, autonomous systems, and healthcare, showcasing its adaptability in handling intricate decision-making assignments. RL has demonstrated promise in the field of healthcare for managing clinical resources, identifying chronic diseases, and improving patient therapy. Robotics uses reinforcement learning (RL) to create autonomous navigation and adaptive motor skills. The study highlights the advantages of reinforcement learning (RL) in managing high-dimensional state spaces, delayed rewards, and model-free learning, but they also point out certain drawbacks, including sample inefficiency and the exploration-exploitation trade-off. The paper highlights the flexibility and potential effect of reinforcement learning (RL) across industries, providing practitioners and academics looking to exploit RL in intelligent systems with insightful information. The future of adaptive decision-making in real-world scenarios may be shaped by RL's integration with other AI approaches, such as deep learning and transfer learning, which could further broaden its applicability to increasingly complicated domains as it continues to advance.